AI 中文总结
本文提出ATTRACT算法，利用PARAFAC张量分解和交替最小二乘，在RIS辅助感知中实现低延迟角度跟踪，通过逐切片处理降低计算开销并保持高精度。
AI 中文摘要
本文提出了一种用于角度轨迹的自适应张量跟踪（ATTRACT）算法，这是一种结构感知的交替最小二乘算法，用于可重构智能表面（RIS）辅助感知中的低延迟目标跟踪。接收回波被表示为动态三阶PARAFAC张量，该张量分离了角度、延迟和多普勒信息。与批量张量估计器不同，ATTRACT每次处理一个数据切片，并携带前一个因子估计，从而避免了在更新目标状态之前收集整个感知窗口的需要。其显著特点是通过已知的发射器-RIS信道、导频信号和RIS配置，将延迟和多普勒结构整合到每次迭代更新中。数值结果表明，对于足够大的RIS，在高信噪比下，ATTRACT实现了与离线张量基线相当的精度，同时降低了计算开销并提供逐时隙估计。
英文摘要
This paper proposes adaptive tensor tracking for angular trajectories (ATTRACT), a structure-aware alternating least squares algorithm for low-latency target tracking in reconfigurable intelligent surface (RIS)-assisted sensing. The received echo is represented as a dynamic third-order PARAFAC tensor that separates angular, delay, and Doppler information. Unlike batch tensor estimators, ATTRACT processes one data slice at a time and carries forward the preceding factor estimates, avoiding the need to collect an entire sensing window before updating the target state. Its distinguishing feature is the integration of delay and Doppler structure into each iterative update through the known transmitter-RIS channel, pilot signals, and RIS configurations. Numerical results show that, for a large enough RIS, ATTRACT achieves accuracy comparable to the offline tensor baseline at high signal-to-noise ratios while reducing computational overhead and providing slot-wise estimates.
